According to findings by the National Institute for Healthcare and Clinical Excellence (NICE), there is evidence that patients are receiving sub-optimal care in hospitals because indications of clinical deterioration were not appreciated or not acted upon in time. Vital signs are typically recorded by nurses three times a day, that is every 8 hours. There is a large body of evidence indicating that during these intervals one or more adverse physiological events flagging patient deterioration are likely to occur.
Different clinicians have expressed the need for monitoring patients continuously, so that the appropriate level of response can be quickly administered to prevent patient deterioration following early warning signs. This has motivated different research and development of monitoring systems that are effective in reducing mortality rates and improving patient outcomes in clinical studies conducted around the world.
In response to this need, Toumaz Healthcare Ltd has developed a patient surveillance system around ultra-low power wireless wearable technologies, as described in EP 1928311 and US 2008-0214946, the contents of which are hereby incorporated by reference. Patients in general wards are patched up with a low cost, light-weight and unobtrusive wireless digital plaster that continuously monitors heart rate (HR), respiration activity and temperature. Thereby, physiological data from patients are transmitted via hotspots (bridges) to one or more servers that enable further analysis and presentation of the values and trends of vital signs in computer monitors. Such central stations are programmed to generate notifications/alerts when the nominal values of one or more of these parameters have exceeded preset limits. Thus, the medical staff is warned about adverse physiological events which may lead to posterior deterioration of the patient if left untreated.
FIG. 2 is a schematic illustration of a portion of a typical ECG for a healthy patient. The heart rate (typically measured in beats per minute) is determined from the time between adjacent R peaks in the ECG signal. Systems that can determine the HR from an ECG signal are known—for example they may use “QRS detection” to identify occurrences of the three peaks labelled “Q”, “R” and “S” in FIG. 2. For example, QRS detection and determination of the HR may be performed according to the Open Source ECG Analysis (OSEA) model, P. S. Hamilton, ‘Open Source ECG Analysis Software (OSEA) Documentation’, www.eplimited.com.
The majority of patients in a general ward are ambulatory in nature. This introduces an additional challenge to early warning monitoring technologies. Motion of a patent often results in artifacts contaminating the physiological signals (such as the ECG) obtained from that patient, owing to electrical signals generated by movement of the patient's muscles. Such noise in a physiological signal not only affects the quality and/or reliability of the processed physiological values, but often increases the incidence of false alerts. Accredited reports have pointed out that a high incidence of false alerts is dangerous as it might lead to the desensitization of the clinical staff to adverse physiological events; and hence, failure to trigger the timely response of the adequate medical teams to patient deterioration—see, for example, Emergency Care Research Institute, “Health Devices—Top 10 Health Technology Hazards for 2013”, 2013.
Unfortunately, the issue of artifacts in physiological signals arising from motion of the patient is complex, since such type of noise is always larger in amplitude and is in-band with the physiological phenomena being measured. This situation is aggravated in the case of low-cost wireless single-lead ECG monitoring technologies, where the absence of additional reference signals from alternative sensors and lack of computational power makes difficult and often impossible to cancel the noise and recover the meaningful information from the physiological data—see, for example S. Choi, and Z. Jiang, “A wearable cardiorespiratory sensor system for analyzing sleep condition”, in Journal of Expert systems with applications; 2008, A. Johansson, “Neural network for photoplethysmorgraphic respiratory rate monitoring”, in Journal of Medical and Biological Engineering and Computing, 2003; or K. Nuzeki, et. al., “Unconstrained cardiorespiratory and body movement monitoring system for home care”, in Proceedings of the International Conference on Acoustics, Speech, and Signal Processing, 2005.
Motion artifacts and other forms of noise exhibit a non-periodic and stochastic (non-deterministic) behavior. This characteristic has been exploited by developers of single-lead ambulatory monitors, in order to discard invalid readings that stem from severely corrupted data—see M. A. Hernandez-Silveira, S. Ang, and A. Burdett, “Wearable Ambulatory Sensor Devices: Challenges and Trade-offs in the Development of Embedded Algorithms for Vital Signs Monitoring”, in the Proceedings of International Conference on Biomedical Engineering, 2013. Typically, the approach involves multivariate statistical estimation and quantification of the regularity and stationarity of the signals to determine the level of confidence in the interpretation or processed value. That is, ECG signals from a patient may be processed to determine both a heart rate and a confidence value associated with the determined heart rate. For example, in the case of single-lead ECG obtained from an ambulatory patient, an error code can be displayed when the confidence level in the determined HR is low (i.e. when the ECG is severely corrupted by noise arising from the patient's movement), instead of presenting the computed heart rate value that would be displayed on the screen if the results of the confidence estimation were high (as would be the case for a heart rate determined from a good quality, low-noise ECG). Unless the number of error codes exceeds a preset threshold (set by the clinical staff), a notification will not be triggered. For healthy individuals with no history of cardiac arrhythmias, we believe that this approach would be effective in its prediction and it would lead to reduction of false alerts.
Unfortunately, this approach is not suitable for patients with certain cardiac abnormal rhythms, such as atrial fibrillation (AF). Atrial fibrillation shows as an aperiodic rhythm in a patient's heartbeat, characterized by irregular inter-beat intervals and rapid ventricular contractions. (In a patient suffering from AF the P-waves shown in FIG. 2 are not present, leading to an abnormal heart rhythm.) It is desirable that a high HR in a patient who suffers from AF would trigger a notification or alert when detected by the monitoring system. Regrettably, however, the opposite would actually occur in a system merely based in the confidence indication approach described above—i.e. the system would deem this abnormal rhythm as noise and assign a low confidence level to the determined HR—and consequently the system would generate error codes, rather than presenting the actual high HR value and triggering the required alert.
Solving this problem is advantageous because AF is very common in the general ward, and is usually first-time diagnosed in a large proportion of the patients during their stay. Furthermore, the number of people who will be suffering from this type of arrhythmia is expected to rise substantially in the next 50 years (M. Cvach, “Monitor Alarm Fatigue: an Integrative Review”, in Journal of Biomedical Instrumentation and Technology, 2012). In addition, this arrhythmia usually leads to fatal conditions if left untreated—i.e. AF is usually associated with the onset of stroke and/or cardiac arrest.
Unfortunately, it is impractical and expensive to fit all patients in the general ward with traditional bedside devices. Instead, a far more attractive solution would be to improve low-cost wireless surveillance systems, to enable them to adequately process the abnormal HR value resulting from an aperiodic heart rhythm such as AF and trigger a notification/alert during its onset.
Many methods for automatic arrhythmia detection exist. These methods are currently incorporated in clinical bedside monitors and portable telemetry and Holter systems, as well as Implantable Medical Devices (IMD) such as pacemakers, for the purpose of arrhythmia detection. Also, in order for accurate diagnosis to take place, multi-lead ECG systems are often required. Some of these techniques are shown below.
U.S. Pat. No. 8,315,699 (Nov. 20, 2012): This method detects and discriminates between supraventricular tachycardia (SVT) from ventricular tachycardia (VT). This involves tracking increases in variability in the heart rate intervals when there is the variability in the RR intervals is low, and vice-versa.
U.S. Pat. No. 8,521,281 (Aug. 27, 2013): The proposed method comprises an implantable medical device that determines the existence of Atrial Fibrillation (AF) based on whether one atrial interval is greater/lesser than pre-determined thresholds, the amplitude of an atrial sensed event is lesser than another pre-determined threshold, and if the intervals from the Ventricular sensed events are irregular.
U.S. Pat. No. 8,521,277 (Aug. 27, 2013): The proposed method comprises an implantable medical device that detects Atrial Fibrillation by provoking an atrial evoked response. The patient is deemed to be suffering from AF if a change in one of the atrial evoked response metrics occurs over time. These metrics include the minimum/maximum amplitude of the response, peak-to-peak interval, as well as duration, area, slope, and timing of the response.
U.S. Pat. No. 8,521,268 (Aug. 27, 2013): The proposed method makes use of a template, encompassing the first two peaks in the signal, and comparing this template with other pairs of peaks within the signal. AF is detected based on the similarities between the template and the signal.
As yet, there is no evidence in the current state of the art reporting or suggesting the use of methods to enhance the reliability of single-lead HR algorithms intended for ambulatory monitoring purposes only.